Solving the Unit Commitment Problem of 10-Generators Set by PSO with Different Spinning-Reserve Values Obtained From Hour-Hour Load Demand Increasing

Abstract

Regarding energy efficiency, the unit contract problem (UCP) is significant. It need to be seen as the assurance of occasions and energy commodities in which the generator offers the most value in addition to having a large amount of energy storage. In this study, we offer a novel approach to solve the UCP issue for a 10-generator test system with variable prices depending on hourly variations in power demand, utilizing particle swarm optimization (PSO). The objective is to keep energy levels sufficient to fulfill demand while reducing the total cost of producing power. Initially, a collection of objects that may hold the key to solving UCP are generated via the suggested PSO technique. Their dedications to the project and the power they create have an impact on everyone's health because of the mobility restriction. Using a combination of their own and other objects' histories, objects may discover the best solution for UCP via the iterative adjustments in speed and location made by the PSO algorithm. This strategy might boost generator economy and efficiency while also resolving the UCP issue. A number of scenarios with various storage factors should be taken into account in order to assess the PSO method's efficacy. The results demonstrate that the cost-confidence ratio is regarded as equal and that the algorithm may converge to the ideal or nearly optimal solution. The efficiency of alternative optimization techniques and the suggested PSO approach are compared using comparative analysis. The findings demonstrate that PSO is more cost-effective than earlier research in identifying the effects of rising storage prices on power consumption between 50 and 100 MW.